ACT-R is a cognitive architecture: a theory for simulating and understanding human cognition. Researchers working on ACT-R strive to understand how people organize knowledge and produce intelligent behavior. As the research continues, ACT-R evolves ever closer into a system which can perform the full range of human cognitive tasks: capturing in great detail the way we perceive, think about, and act on the world.

On the exterior, ACT-R looks like a programming language; however, its constructs reflect assumptions about human cognition. These assumptions are based on numerous facts derived from psychology experiments. Like a programming language, ACT-R is a framework: for different tasks (e.g., Tower of Hanoi, memory for text or for list of words, language comprehension, communication, aircraft controlling), researchers create models (aka programs) that are written in ACT-R and that, beside incorporating the ACT-R's view of cognition, add their own assumptions about the particular task. These assumptions can be tested by comparing the results of the model with the results of people doing the same tasks.

ACT-R is a hybrid cognitive architecture. Its symbolic structure is a production system; the subsymbolic structure is represented by a set of massively parallel processes that can be summarized by a number of mathematical equations. The subsymbolic equations control many of the symbolic processes. For instance, if several productions match the state of the buffers, a subsymbolic utility equation estimates the relative cost and benefit associated with each production and decides to select for execution the production with the highest utility. Similarly, whether (or how fast) a fact can be retrieved from declarative memory depends on subsymbolic retrieval equations, which take into account the context and the history of usage of that fact. Subsymbolic mechanisms are also responsible for most learning processes in ACT-R.

AERA is a cognitive architecture - and a blueprint - for constructing agents with high levels of operational autonomy, starting from only a small amount of designer-specified code – a seed. Using a value-driven dynamic priority scheduling to control the parallel execution of a vast number of lines of reasoning, the system accumulates increasingly useful models of its experience, resulting in recursive self-improvement that can be autonomously sustained after the machine leaves the lab, within the boundaries imposed by its designers.

AERA demonstrates domain-independent self-supervised cumulative learning of complex tasks. Unlike contemporary AI systems, AERA-based agents excel at handling novelty - situations, information, data, tasks - that their programmers could not anticipate. It is the only implementable / implemented system in existence for achieving bounded recursive self-improvement.

AERA-based agents learn cumulatively from experience by interacting with the world and generating compositional causal-relational micro-models of its experience. Using non-axiomatic abduction and deduction, it constantly predicts how to achieve its active goals and what the future may hold, generating a flexible opportunistically-interruptable plan for action.

An important observation is that most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility function.

Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible.

The major drawback of the AIXI model is that it is uncomputable, ... which makes an implementation impossible. To overcome this problem, we constructed a modified model AIXItl, which is still effectively more intelligent than any other time t and length l bounded algorithm.

Vast amounts of commonsense knowledge, representing human consensus reality, would need to be encoded to produce a general AI system. In order to mimic human reasoning, Cyc would require background knowledge regarding science, society and culture, climate and weather, money and financial systems, health care, history, politics, and many other domains of human experience. The Cyc Project team expected to encode at least a million facts spanning these and many other topic areas.

The Cyc knowledge base (KB) is a formalized representation of a vast quantity of fundamental human knowledge: facts, rules of thumb, and heuristics for reasoning about the objects and events of everyday life. The medium of representation is the formal language CycL. The KB consists of terms -- which constitute the vocabulary of CycL -- and assertions which relate those terms. These assertions include both simple ground assertions and rules.

We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

The combination of the generality of GPT-4's capabilities, with numerous abilities spanning a broad swath of domains, and its performance on a wide spectrum of tasks at or beyond human-level, makes us comfortable with saying that GPT-4 is a significant step towards AGI.

At the core of every Grok model is the Cortical Learning Algorithm (CLA), a detailed and realistic model of a layer of cells in the neocortex. Contrary to popular belief, the neocortex is not a computing system, it is a memory system. When you are born, the neocortex has structure but virtually no knowledge. You learn about the world by building models of the world from streams of sensory input. From these models, we make predictions, detect anomalies, and take actions.

In other words, the brain can best be described as a predictive modeling system that turns predictions into actions. Three key operating principles of the neocortex are described below: sparse distributed representations, sequence memory, and on-line learning.

Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive "theory of everything." With modules or processes for perception, working memory, episodic memories, "consciousness," procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA technology is based on the LIDA cognitive cycle, a sort of "cognitive atom." The more elementary cognitive modules play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles.

The LIDA architecture represents perceptual entities, objects, categories, relations, etc., using nodes and links .... These serve as perceptual symbols acting as the common currency for information throughout the various modules of the LIDA architecture.

The MicroPsi agent architecture describes the interaction of emotion, motivation and cognition of situated agents, mainly based on the Psi theory of Dietrich Dorner. The Psi theory addresses emotion, perception, representation and bounded rationality, but being formulated within psychology, has had relatively little impact on the discussion of agents within computer science. MicroPsi is a formulation of the original theory in a more abstract and formal way, at the same time enhancing it with additional concepts for memory, building of ontological categories and attention.

The agent framework uses semantic networks, called node nets, that are a unified representation for control structures, plans, sensory and action schemas, Bayesian networks and neural nets. Thus it is possible to set up different kinds of agents on the same framework.

What makes NARS different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. NARS attempts to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, etc, so as to provide a unified theory, model, and system for AI as a whole. The ultimate goal of this research is to build a thinking machine.

The development of NARS takes an incremental approach consisting four major stages. At each stage, the logic is extended to give the system a more expressive language, a richer semantics, and a larger set of inference rules; the memory and control mechanism are then adjusted accordingly to support the new logic.

In NARS the notion of "reasoning" is extended to represent a system's ability to predict the future according to the past, and to satisfy the unlimited resources demands using the limited resources supply, by flexibly combining justifiable micro steps into macro behaviors in a domain-independent manner.

OpenCog, as a software framework, aims to provide research scientists and software developers with a common platform to build and share artificial intelligence programs. The long-term goal of OpenCog is acceleration of the development of beneficial AGI.

OpenCogPrime is a specific AGI design being constructed within the OpenCog framework. It comes with a fairly detailed, comprehensive design covering all aspects of intelligence. The hypothesis is that if this design is fully implemented and tested on a reasonably-sized distributed network, the result will be an AGI system with general intelligence at the human level and ultimately beyond.

While an OpenCogPrime based AGI system could do a lot of things, we are initially focusing on using OpenCogPrime to control simple virtual agents in virtual worlds. We are also experimenting with using it to control a Nao humanoid robot. See http://novamente.net/example for some illustrative videos.

The goal of this effort is to develop a sufficiently efficient, functionally elegant, generically cognitive, grand unified, cognitive architecture in support of virtual humans (and hopefully intelligent agents/robots – and even a new form of unified theory of human cognition – as well).

Our focus is on the development of the Sigma (∑) architecture, which explores the graphical architecture hypothesis that progress at this point depends on blending what has been learned from over three decades worth of independent development of cognitive architectures and graphical models, a broadly applicable state-of-the-art formalism for constructing intelligent mechanisms. The result is a hybrid (discrete+continuous) mixed (symbolic+probabilistic) approach that has yielded initial results across memory and learning, problem solving and decision making, mental imagery and perception, speech and natural language, and emotion and attention.

The long term goal of the SNePS Research Group is to understand the nature of intelligent cognitive processes by developing and experimenting with computational cognitive agents that are able to use and understand natural language, reason, act, and solve problems in a wide variety of domains.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as logical sentences. The effect is that propositions can occur as arguments of propositions, acts, and policies without limit, and without leaving first-order logic.